CellFlow builds on flow matching, optimal transport & attention mechanisms to learn an embedding of complex experimental conditions. This guides the flow from control to perturbed cells, generating realistic states while minimizing displacement costs.
From cell lines to full embryos, drug treatments to genetic perturbations, neuron engineering to virtual organoid screens — odds are there’s something in it for you!
Built on flow matching, CellFlow can help guide your next phenotypic screen: biorxiv.org/content/10.1101/2025.04.11.648220v1
We predicted donor-specific cytokine responses on 10 million cells! We found CellFlow to exhibit scaling laws in the number of seen conditions and gained interpretable insights into model training.
Check out the paper for more applications, including cell fate engineering and organoid protocol optimisation!
Head to cellflow.readthedocs.io for tutorials, and get in touch—Plenty of exciting directions to explore!
We let CellFlow learn the perturbed development of entire embryos, allowing to model the continuous trajectories of single cells under different genetic perturbations.
CellFlow was a highly collaborative team effort. Thanks to the great co-lead @josch1.bsky.social , and the fantastic team Daniil, Lea, Soeren, Alessandro, @le-and-er.bsky.social, Alejandro, @guillaumehu.bsky.social, @hsiuchuanlin.bsky.social, @nazbukina.bsky.social, Fatima, Theo,
@chatgtp.bsky.social, Manuel, A. Regev, B. Treutlein, @graycamplab.bsky.social, @fabiantheis.bsky.social
We found CellFlow to consistently perform competitively across various benchmarks, including drug and genetic perturbation screens - while being able to address complex experimental setups other methods are not able to address.
Also check out the cell fate engineering applications:
bsky.app/profile/josc...
Yay, we built a thing! With @dominik1klein.bsky.social Daniil, Aviv Regev, Barbara Treutlein @graycamplab.bsky.social @fabiantheis.bsky.social we use flow matching to enable generalised sc phenotype modeling. From cytokine screens to fate programming and organoid engineering tinyurl.com/3xhju7db
